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gpustack/
FLUX.1-schnell-GGUF

Name: FLUX.1-schnell-GGUF
Availability: InStock

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FLUX.1-schnell-GGUF

!!! Experimental supported by gpustack/llama-box v0.0.77+ only !!!

Model creator: Black Forest Labs
Original model: FLUX.1-schnell
GGUF quantization: based on stable-diffusion.cpp ac54e that patched by llama-box.

Quantization	OpenAI CLIP ViT-L/14 Quantization	Google T5-xxl Quantization	VAE Quantization
FP16	FP16	FP16	FP16
Q8_0	FP16	Q8_0	FP16
(pure) Q8_0	Q8_0	Q8_0	FP16
Q4_1	FP16	Q8_0	FP16
Q4_0	FP16	Q8_0	FP16
(pure) Q4_0	Q4_0	Q4_0	FP16

FLUX.1 [schnell] is a 12 billion parameter rectified flow transformer capable of generating images from text descriptions. For more information, please read our blog post.

Key Features

Cutting-edge output quality and competitive prompt following, matching the performance of closed source alternatives.
Trained using latent adversarial diffusion distillation, FLUX.1 [schnell] can generate high-quality images in only 1 to 4 steps.
Released under the apache-2.0 licence, the model can be used for personal, scientific, and commercial purposes.

Usage

We provide a reference implementation of FLUX.1 [schnell], as well as sampling code, in a dedicated github repository. Developers and creatives looking to build on top of FLUX.1 [schnell] are encouraged to use this as a starting point.

API Endpoints

The FLUX.1 models are also available via API from the following sources

bfl.ml (currently FLUX.1 [pro])
replicate.com
fal.ai
mystic.ai

ComfyUI

FLUX.1 [schnell] is also available in Comfy UI for local inference with a node-based workflow.

Diffusers

To use FLUX.1 [schnell] with the 🧨 diffusers python library, first install or upgrade diffusers

pip install -U diffusers

Then you can use FluxPipeline to run the model

python

import torch
from diffusers import FluxPipeline

pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power

prompt = "A cat holding a sign that says hello world"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
    generator=torch.Generator("cpu").manual_seed(0)
).images[0]
image.save("flux-schnell.png")

To learn more check out the diffusers documentation

Limitations

This model is not intended or able to provide factual information.
As a statistical model this checkpoint might amplify existing societal biases.
The model may fail to generate output that matches the prompts.
Prompt following is heavily influenced by the prompting-style.

Out-of-Scope Use

The model and its derivatives may not be used

In any way that violates any applicable national, federal, state, local or international law or regulation.
For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; including but not limited to the solicitation, creation, acquisition, or dissemination of child exploitative content.
To generate or disseminate verifiably false information and/or content with the purpose of harming others.
To generate or disseminate personal identifiable information that can be used to harm an individual.
To harass, abuse, threaten, stalk, or bully individuals or groups of individuals.
To create non-consensual nudity or illegal pornographic content.
For fully automated decision making that adversely impacts an individual's legal rights or otherwise creates or modifies a binding, enforceable obligation.
Generating or facilitating large-scale disinformation campaigns.

gpustack/FLUX.1-schnell-GGUF